Impairment of efferocytosis in apoptotic macrophages is a known determinant of the severity of atherosclerosis and the vulnerability of plaques to rupture. The precise mechanisms involved in impaired efferocytosis are unclear. Given the well-recognized role of the inflammatory cytokine cyclophilin A (Cyp A) in modulating several atherogenic mechanisms in high-glucose primed monocytes, we investigated the role of Cyp A in macrophage efferocytosis. The efficiency of efferocytosis in RAW 264.7 macrophages grown in vitro and primed with cyclophilin A was assessed using flow cytometry and confocal assays. Cholesterol content in cells was measured using cell-based cholesterol efflux assay. Proteomic analysis and bioinformatics tools were employed to decipher the link between cyclophilin A and the known ligand receptors involved in efferocytosis. Cyclophilin A was found to impair efferocytosis in apoptotic macrophages by reducing ABCA1-mediated cholesterol efflux in foam cells derived from macrophages. Cyclophilin A-primed macrophages showed an increase in expression of the don’t-eat-me signal CD 47 and a decrease in the expression of the eat-me signal, calreticulin. Phagocytosis was restored upon silencing of cyclophilin A. New Zealand white rabbits were fed a high-fat diet, and lesions in their aortae were analyzed histologically for evidence of atherosclerosis and the expression of Cyp A, CD 47 and calreticulin, the ligand receptor involved in efferocytosis. Gene and protein expressions in aortae and macrophages were analyzed by real-time PCR and Western blotting. Cyclophilin A, via its effects on the expression of CD 47 and calreticulin, impairs efferocytosis in apoptotic macrophages. Together with its impact on cholesterol efflux from macrophages, these effects can amplify other mechanisms of Cyp A in accelerating the progression of atherosclerosis.

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa , another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named “HNCDrugResDb” was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

MicroRNAs are single-stranded non-coding RNA sequences of 18 - 24 nucleotides in length. They play an important role in post-transcriptional regulation of gene expression. Evidences of microRNA acting as promoter/suppressor of several diseases including cancer are being unveiled. Recent studies have shown that microRNAs are differentially expressed in disease states when compared with that of normal states. Profiling of microRNA is a good measure to estimate the differences in expression levels, which can be further utilized to understand the progression of any associated disease. Machine learning techniques, when applied to microRNA expression values obtained from NGS data, could be utilized for the development of effective disease prediction system. This paper discusses an approach for microRNA expression profiling, its normalization and a Support Vector based machine learning technique to develop a Cancer Prediction System. Presently, the system has been trained with data samples of hepatocellular carcinoma, carcinomas of the bladder and lung cancer. microRNAs related to specific types of cancer were used to build the classifier. When the system is trained and tested with 10 fold cross validation, the prediction accuracy obtained is 97.56% for lung cancer, 97.82% for hepatocellular carcinoma and 95.0% for carcinomas of the bladder. The system is further validated with separate test sets, which show accuracies higher than 90%. A ranking based on differential expression marks the relative significance of each microRNA in the prediction process. Results from experiments proved that microRNA expression profiling is an effective mechanism for disease identification, provided sufficiently large database is available.

To define the growing significance of cellular targets and/or effectors of cancer drugs, we examined the fitness dependency of cellular targets and effectors of cancer drug targets across human cancer cells from 19 cancer types. We observed that the deletion of 35 out of 47 cellular effectors and/or targets of oncology drugs did not result in the expected loss of cell fitness in appropriate cancer types for which drugs targeting or utilizing these molecules for their actions were approved. Additionally, our analysis recognized 43 cellular molecules as fitness genes in several cancer types in which these drugs were not approved, and thus, providing clues for repurposing certain approved oncology drugs in such cancer types. For example, we found a widespread upregulation and fitness dependency of several components of the mevalonate and purine biosynthesis pathways (currently targeted by bisphosphonates, statins, and pemetrexed in certain cancers) and an association between the overexpression of these molecules and reduction in the overall survival duration of patients with breast and other hard-to-treat cancers, for which such drugs are not approved. In brief, the present analysis raised cautions about off-target and undesirable effects of certain oncology drugs in a subset of cancers where the intended cellular effectors of drug might not be good fitness genes and that this study offers a potential rationale for repurposing certain approved oncology drugs for targeted therapeutics in additional cancer types.

Over the last three decades, p21-activated kinases (PAKs) have emerged as prominent intracellular nodular signaling molecules in cancer cells with a spectrum of cancer-promoting functions ranging from cell survival to anchorage-independent growth to cellular invasiveness. As PAK family members are widely overexpressed and/or hyperactivated in a variety of human tumors, over the years PAKs have also emerged as therapeutic targets, resulting in the development of clinically relevant PAK inhibitors. Over the last two decades, this has been a promising area of active investigation for several academic and pharmaceutical groups. Similar to other kinases, blocking the activity of one PAK family member leads to compensatory activity on the part of other family members. Because PAKs are also activated by stress-causing anticancer drugs, PAKs are components in the rewiring of survival pathways in the action of several therapeutic agents; in turn, they contribute to the development of therapeutic resistance. This, in turn, creates an opportunity to co-target the PAKs to achieve a superior anticancer cellular effect. Here we discuss the role of PAKs and their effector pathways in the modulation of cellular susceptibility to cancer therapeutic agents and therapeutic resistance.